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Authors: Jacob Langner 1 ; Hannes Grolig 1 ; Stefan Otten 1 ; Marc Holzäpfel 2 and Eric Sax 1

Affiliations: 1 FZI Research Center for Information Technology, 76131 Karlsruhe and Germany ; 2 Dr. Ing. h.c. F. Porsche AG, 71287 Weissach and Germany

ISBN: 978-989-758-374-2

Keyword(s): Scenario Extraction, Logical Scenarios, Vehicle Environment, Real-World-Driving-Data.

Abstract: For the development of Advanced Driver Assistant Systems (ADAS) and Automated Driving Systems (ADS) a change from test case-based testing towards scenario-based testing can be observed. Based on current approaches to define scenarios and their inherent problems, we identify the need to extract scenarios including the static environment from recorded real-world-driving-data. We present an approach, that solves the problem to extract dynamic-length-segments containing a single scenario. These segments are enriched with a feature vector with information relevant for the feature under test. By clustering these scenarios a logical scenario catalog is created, containing all scenarios within the test data. Corner cases are represented as well as common scenarios. An accumulated total length can be calculated for each logical scenario, giving a brief understanding about existing test coverage of the scenario.

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Paper citation in several formats:
Langner, J.; Grolig, H.; Otten, S.; Holzäpfel, M. and Sax, E. (2019). Logical Scenario Derivation by Clustering Dynamic-Length-Segments Extracted from Real-World-Driving-Data.In Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-374-2, pages 458-467. DOI: 10.5220/0007723304580467

@conference{vehits19,
author={Jacob Langner. and Hannes Grolig. and Stefan Otten. and Marc Holzäpfel. and Eric Sax.},
title={Logical Scenario Derivation by Clustering Dynamic-Length-Segments Extracted from Real-World-Driving-Data},
booktitle={Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2019},
pages={458-467},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007723304580467},
isbn={978-989-758-374-2},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - Logical Scenario Derivation by Clustering Dynamic-Length-Segments Extracted from Real-World-Driving-Data
SN - 978-989-758-374-2
AU - Langner, J.
AU - Grolig, H.
AU - Otten, S.
AU - Holzäpfel, M.
AU - Sax, E.
PY - 2019
SP - 458
EP - 467
DO - 10.5220/0007723304580467

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